{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Pipeline and Productionisation of Machine Learning Models (Model Serialization)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Loading the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('data/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 6)\n"
     ]
    }
   ],
   "source": [
    "print(iris.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ed03a7b580>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1393x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue = 'Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 1 - Identify Input and Output**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]\n",
    "\n",
    "y = iris['Species']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 2 - Split the Data into Train and Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-24T14:42:24.638088Z",
     "start_time": "2018-08-24T14:42:24.630111Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112, 4) (38, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 3 - Data Preprocessing on train data (X_train)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# scaling the numerical features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# column names are (annoyingly) lost after Scaling\n",
    "# (i.e. the dataframe is converted to a numpy ndarray)\n",
    "\n",
    "X_train_rescaled = scaler.fit_transform(X_train)\n",
    "\n",
    "print(type(X_train_rescaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.015440</td>\n",
       "      <td>-0.119255</td>\n",
       "      <td>0.225127</td>\n",
       "      <td>0.356382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>-0.099845</td>\n",
       "      <td>-1.040395</td>\n",
       "      <td>0.113560</td>\n",
       "      <td>-0.028648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1.053005</td>\n",
       "      <td>-0.119255</td>\n",
       "      <td>0.950314</td>\n",
       "      <td>1.126441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.367980</td>\n",
       "      <td>0.341315</td>\n",
       "      <td>-1.392599</td>\n",
       "      <td>-1.312081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.168290</td>\n",
       "      <td>0.111030</td>\n",
       "      <td>0.727180</td>\n",
       "      <td>1.383128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "61        0.015440     -0.119255       0.225127      0.356382\n",
       "92       -0.099845     -1.040395       0.113560     -0.028648\n",
       "112       1.053005     -0.119255       0.950314      1.126441\n",
       "2        -1.367980      0.341315      -1.392599     -1.312081\n",
       "141       1.168290      0.111030       0.727180      1.383128"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rescaled = pd.DataFrame(X_train_rescaled, \n",
    "                                columns = scaler.get_feature_names_out(), \n",
    "                                index = X_train.index)\n",
    "\n",
    "X_train_rescaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: ['SepalLengthCm' 'SepalWidthCm' 'PetalLengthCm' 'PetalWidthCm']\n"
     ]
    }
   ],
   "source": [
    "print(\"Features:\", scaler.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation: [0.86741565 0.43424445 1.79264014 0.77916047]\n",
      "Mean: [5.88660714 3.05178571 3.79642857 1.22232143]\n",
      "Sample Size: 112\n"
     ]
    }
   ],
   "source": [
    "print(\"Standard Deviation:\", scaler.scale_)\n",
    "print(\"Mean:\", scaler.mean_)\n",
    "print(\"Sample Size:\", scaler.n_samples_seen_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    5.886607\n",
       "SepalWidthCm     3.051786\n",
       "PetalLengthCm    3.796429\n",
       "PetalWidthCm     1.222321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0.871314\n",
       "SepalWidthCm     0.436196\n",
       "PetalLengthCm    1.800697\n",
       "PetalWidthCm     0.782662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.std()\n",
    "\n",
    "# By default, ddof=1 \n",
    "# Reference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.std.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Why is there a difference? 😥**\n",
    "\n",
    "Documentation says:  \n",
    "We use a biased estimator for the standard deviation, equivalent to `numpy.std(x, ddof=0)`. Note that the choice of ddof is unlikely to affect model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0.867416\n",
       "SepalWidthCm     0.434244\n",
       "PetalLengthCm    1.792640\n",
       "PetalWidthCm     0.779160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.std(ddof=0)\n",
    "\n",
    "# Worked? 😱"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 4 - Data Preprocessing on Test Data (X_test)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>-0.099845</td>\n",
       "      <td>-0.579825</td>\n",
       "      <td>0.727180</td>\n",
       "      <td>1.511471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.130725</td>\n",
       "      <td>-1.961535</td>\n",
       "      <td>0.113560</td>\n",
       "      <td>-0.285335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.445700</td>\n",
       "      <td>2.644166</td>\n",
       "      <td>-1.336815</td>\n",
       "      <td>-1.312081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1.629430</td>\n",
       "      <td>-0.349540</td>\n",
       "      <td>1.396583</td>\n",
       "      <td>0.741412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.022125</td>\n",
       "      <td>0.801885</td>\n",
       "      <td>-1.281032</td>\n",
       "      <td>-1.312081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "114      -0.099845     -0.579825       0.727180      1.511471\n",
       "62        0.130725     -1.961535       0.113560     -0.285335\n",
       "33       -0.445700      2.644166      -1.336815     -1.312081\n",
       "107       1.629430     -0.349540       1.396583      0.741412\n",
       "7        -1.022125      0.801885      -1.281032     -1.312081"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_rescaled = pd.DataFrame(scaler.transform(X_test), \n",
    "                                   columns = scaler.get_feature_names_out(), \n",
    "                                   index = X_test.index)\n",
    "\n",
    "X_test_rescaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 5 - Building a Model (i.e. Train the classifier)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-24T14:42:27.982991Z",
     "start_time": "2018-08-24T14:42:25.444548Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_classifier = KNeighborsClassifier()\n",
    "\n",
    "knn_classifier.fit(X_train_rescaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 6 - Evaluating on Train Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9642857142857143"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# make class predictions for X_train_dtm\n",
    "y_train_pred = knn_classifier.predict(X_train_rescaled)\n",
    "\n",
    "metrics.accuracy_score(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Step 7 - Evaluate on Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-24T14:42:28.781204Z",
     "start_time": "2018-08-24T14:42:28.778211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = knn_classifier.predict(X_test_rescaled)\n",
    "\n",
    "metrics.accuracy_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Is this the BEST MODEL?**\n",
    "\n",
    "Upon initial assessment, it appears that the model exhibits the following characteristics:\n",
    "1. High train_score, indicating that the algorithm effectively learned from the training data.\n",
    "2. High test_score, suggesting that the model generalizes well when applied to unseen testing data.\n",
    "\n",
    "It is imperative to conduct a thorough evaluation of model performance to determine the optimal solution. While the initial assessment reveals promising characteristics such as high train and test scores, it is essential to delve deeper into additional factors to ascertain the best model for the task at hand.\n",
    "\n",
    "**Key Considerations:**\n",
    "\n",
    "1. **Model Size**: Assessing the complexity and resource requirements of the model is crucial. A larger model size may indicate increased computational overhead and storage requirements.\n",
    "\n",
    "2. **Training Time**: Evaluating the time taken to train the model provides insights into computational efficiency and resource utilization during the training phase. Minimizing training time is essential for scalable and efficient model development.\n",
    "\n",
    "3. **Prediction Time**: Examining the speed and efficiency of model predictions in real-world scenarios is paramount. Models with faster prediction times are more suitable for deployment in production environments.\n",
    "\n",
    "By meticulously considering these additional criteria alongside traditional performance metrics, we can make a more informed decision regarding the suitability and effectiveness of the model.\n",
    "\n",
    "**Identified Problems:**\n",
    "\n",
    "In addition to evaluating model performance, it is essential to address underlying issues in the current approach:\n",
    "\n",
    "1. **Efficiency**: The current code may lack efficiency, potentially leading to suboptimal performance and resource utilization.\n",
    "\n",
    "2. **Reproducibility**: Ensuring reproducibility is essential for maintaining consistency and reliability across different environments and datasets.\n",
    "\n",
    "3. **Scalability**: Scalability is paramount for accommodating larger datasets and increasing computational demands as the project evolves.\n",
    "\n",
    "**Answering Key Questions:**\n",
    "\n",
    "1. **Algorithm Flexibility**: The number of algorithms that can be applied is crucial for exploring various modeling approaches and selecting the most suitable one.\n",
    "\n",
    "2. **Model Variability**: Each algorithm's ability to generate more than one model enables comprehensive exploration of hyperparameters and configurations, facilitating model optimization.\n",
    "\n",
    "3. **Data Preprocessing Techniques**: The capability to apply diverse data preprocessing techniques enhances the model's adaptability and robustness to different types of data.\n",
    "\n",
    "**Exploring Permutations and Combinations:**\n",
    "\n",
    "Considering all possible combinations of algorithms, model variations, and data preprocessing techniques allows for a comprehensive exploration of the solution space. This exhaustive analysis is essential for identifying the most effective model configuration that meets project requirements and objectives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Introducing Pipelines for Optimal Workflow**\n",
    "\n",
    "Pipelines help improve the efficiency, reproducibility, and scalability of the machine learning workflow by automating and standardizing common tasks and processes. They also promote cleaner and more modular code, making it easier to maintain and extend machine learning models over time.\n",
    "\n",
    "**How does Pipeline work?**  \n",
    "Pipeline allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final predictor for predictive modeling.\n",
    "\n",
    "Intermediate steps of the pipeline must be ‘transformers’, that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using memory argument.\n",
    "\n",
    "Pipelines are important because \n",
    "1. **Efficient Hyperparameter Tuning:** Using pipelines also allows for hyperparameter tuning and cross-validation to be performed more efficiently, as the entire pipeline can be treated as a single estimator. This makes it easier to search over a space of hyperparameters and evaluate different configurations. Overall, pipelines promote code modularity, reusability, and reproducibility in machine learning projects.\n",
    "2. **Automate ML Workflows:** They help automate machine learning workflows by sequentially applying a series of preprocessing steps followed by a model fitting step. This ensures that all preprocessing steps are applied consistently to both the training and testing data, avoiding data leakage and making the code more readable and maintainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n",
      "CPU times: total: 5.66 s\n",
      "Wall time: 5.72 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;classifier&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid=[{&#x27;classifier__n_neighbors&#x27;: [3, 5, 7, 9, 11, 13, 15,\n",
       "                                                      17, 19],\n",
       "                          &#x27;classifier__p&#x27;: [1, 2, 3],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]}],\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;classifier&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid=[{&#x27;classifier__n_neighbors&#x27;: [3, 5, 7, 9, 11, 13, 15,\n",
       "                                                      17, 19],\n",
       "                          &#x27;classifier__p&#x27;: [1, 2, 3],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]}],\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;classifier&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                       ('classifier', KNeighborsClassifier())]),\n",
       "             param_grid=[{'classifier__n_neighbors': [3, 5, 7, 9, 11, 13, 15,\n",
       "                                                      17, 19],\n",
       "                          'classifier__p': [1, 2, 3],\n",
       "                          'scaler': [StandardScaler(), MinMaxScaler()]}],\n",
       "             return_train_score=True, scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define pipeline steps\n",
    "pipe_1 = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', KNeighborsClassifier())\n",
    "    ]\n",
    ")\n",
    "\n",
    "N_NEIGHBORS = [i for i in range(3, 21, 2)]\n",
    "P = [1, 2, 3]\n",
    "\n",
    "# Observe the Key Value Pair format\n",
    "parameter_grid_1 = [\n",
    "    {\n",
    "        'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "        'classifier__n_neighbors' : N_NEIGHBORS,              \n",
    "        'classifier__p' : P\n",
    "    }\n",
    "]\n",
    "\n",
    "clf = GridSearchCV(\n",
    "    estimator=pipe_1, \n",
    "    param_grid=parameter_grid_1, \n",
    "    scoring='accuracy',\n",
    "    cv=5,\n",
    "    return_train_score=True,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "%time clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator found on train set\n",
      "Pipeline(steps=[('scaler', MinMaxScaler()),\n",
      "                ('classifier', KNeighborsClassifier(n_neighbors=3))])\n",
      "\n",
      "Score on Test Data:  0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "print(\"Best estimator found on train set\")\n",
    "print(clf.best_estimator_)\n",
    "print()\n",
    "\n",
    "print('Score on Test Data: ', clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Serialization and Deserialization**\n",
    "\n",
    "Serialization is the process of converting a Python object into a byte stream, which can then be stored in a file or transmitted over a network. Deserialization is the reverse process, where the byte stream is converted back into a Python object.\n",
    "\n",
    "In the context of the `joblib` library in Python:\n",
    "\n",
    "- **Serialization**: In `joblib`, serialization refers to the process of converting a Python object into a byte stream using the `joblib.dump()` function. This byte stream can then be written to a file or transmitted over a network.\n",
    "\n",
    "- **Deserialization**: Deserialization in `joblib` involves reading a byte stream from a file or network and converting it back into a Python object using the `joblib.load()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_models/demo_model_knn.pkl']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Serialization\n",
    "\n",
    "best_model = clf.best_estimator_\n",
    "\n",
    "joblib.dump(best_model, 'best_models/demo_model_knn.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: ['Iris-setosa']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Deserialization\n",
    "\n",
    "model = joblib.load('best_models/demo_model_knn.pkl')\n",
    "\n",
    "new_data = np.array([[5.1, 3.0, 1.1, 0.1]])\n",
    "\n",
    "prediction = model.predict(new_data)\n",
    "\n",
    "print(\"Prediction:\", prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 7.2 ms\n",
      "Accuracy Score: 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "# Let's analyse the models prediction time and model size\n",
    "\n",
    "%time y_test_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\", metrics.accuracy_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Size: 11965 Bytes\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Size:\", os.path.getsize('best_models/demo_model_knn.pkl'), \"Bytes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Finding the best SVM model using Effective Pipelining and Productionization of the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n",
      "CPU times: total: 3.39 s\n",
      "Wall time: 3.37 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;classifier&#x27;, SVC())]),\n",
       "             param_grid=[{&#x27;classifier__C&#x27;: [0.1, 0.01, 1, 10, 100],\n",
       "                          &#x27;classifier__kernel&#x27;: [&#x27;rbf&#x27;],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]},\n",
       "                         {&#x27;classifier__C&#x27;: [0.1, 0.01, 1, 10, 100],\n",
       "                          &#x27;classifier__degree&#x27;: [2, 3, 4, 5],\n",
       "                          &#x27;classifier__kernel&#x27;: [&#x27;poly&#x27;],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]},\n",
       "                         {&#x27;classifier__C&#x27;: [0.1, 0.01, 1, 10, 100],\n",
       "                          &#x27;classifier__kernel&#x27;: [&#x27;linear&#x27;],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]}],\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;classifier&#x27;, SVC())]),\n",
       "             param_grid=[{&#x27;classifier__C&#x27;: [0.1, 0.01, 1, 10, 100],\n",
       "                          &#x27;classifier__kernel&#x27;: [&#x27;rbf&#x27;],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]},\n",
       "                         {&#x27;classifier__C&#x27;: [0.1, 0.01, 1, 10, 100],\n",
       "                          &#x27;classifier__degree&#x27;: [2, 3, 4, 5],\n",
       "                          &#x27;classifier__kernel&#x27;: [&#x27;poly&#x27;],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]},\n",
       "                         {&#x27;classifier__C&#x27;: [0.1, 0.01, 1, 10, 100],\n",
       "                          &#x27;classifier__kernel&#x27;: [&#x27;linear&#x27;],\n",
       "                          &#x27;scaler&#x27;: [StandardScaler(), MinMaxScaler()]}],\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;classifier&#x27;, SVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                       ('classifier', SVC())]),\n",
       "             param_grid=[{'classifier__C': [0.1, 0.01, 1, 10, 100],\n",
       "                          'classifier__kernel': ['rbf'],\n",
       "                          'scaler': [StandardScaler(), MinMaxScaler()]},\n",
       "                         {'classifier__C': [0.1, 0.01, 1, 10, 100],\n",
       "                          'classifier__degree': [2, 3, 4, 5],\n",
       "                          'classifier__kernel': ['poly'],\n",
       "                          'scaler': [StandardScaler(), MinMaxScaler()]},\n",
       "                         {'classifier__C': [0.1, 0.01, 1, 10, 100],\n",
       "                          'classifier__kernel': ['linear'],\n",
       "                          'scaler': [StandardScaler(), MinMaxScaler()]}],\n",
       "             return_train_score=True, scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_2 = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', SVC())\n",
    "    ]\n",
    ")\n",
    "\n",
    "DEGREE = [2, 3, 4, 5]\n",
    "C = [0.1, 0.01, 1, 10, 100]\n",
    "\n",
    "\n",
    "# Observe the Key Value Pair format\n",
    "parameter_grid_2 = [\n",
    "    {\n",
    "        'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "        'classifier__kernel' : ['rbf'], \n",
    "        'classifier__C' : C\n",
    "    }, \n",
    "    {\n",
    "        'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "        'classifier__kernel' : ['poly'], \n",
    "        'classifier__degree' : DEGREE, \n",
    "        'classifier__C' : C\n",
    "    }, \n",
    "    {\n",
    "        'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "        'classifier__kernel' : ['linear'], \n",
    "        'classifier__C' : C\n",
    "    }\n",
    "]\n",
    "\n",
    "clf = GridSearchCV(\n",
    "    estimator=pipe_2, \n",
    "    param_grid=parameter_grid_2, \n",
    "    scoring='accuracy',\n",
    "    cv=5,\n",
    "    return_train_score=True,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "%time clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator found on train set\n",
      "Pipeline(steps=[('scaler', MinMaxScaler()), ('classifier', SVC(C=1))])\n",
      "\n",
      "Score on Test Data:  0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "print(\"Best estimator found on train set\")\n",
    "print(clf.best_estimator_)\n",
    "print()\n",
    "\n",
    "print('Score on Test Data: ', clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_models/demo_model_svc.pkl']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Serialization\n",
    "\n",
    "best_model = clf.best_estimator_\n",
    "\n",
    "joblib.dump(best_model, 'best_models/demo_model_svc.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: ['Iris-setosa']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Deserialization\n",
    "\n",
    "model = joblib.load('best_models/demo_model_svc.pkl')\n",
    "\n",
    "new_data = np.array([[5.1, 3.0, 1.1, 0.1]])\n",
    "\n",
    "prediction = model.predict(new_data)\n",
    "\n",
    "print(\"Prediction:\", prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 2.6 ms\n",
      "Accuracy Score 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "# Let's analyse the models prediction time and model size\n",
    "\n",
    "%time y_test_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score\", metrics.accuracy_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Size: 5634 Bytes\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Size:\", os.path.getsize('best_models/demo_model_svc.pkl'), \"Bytes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Writting Even More Complicated Pipelines to Automate the Workflow**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** knn **********\n",
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n",
      "CPU times: total: 5.84 s\n",
      "Wall time: 5.93 s\n",
      "Score on Test Data:  0.9736842105263158\n",
      "********** svc **********\n",
      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n",
      "CPU times: total: 3.48 s\n",
      "Wall time: 3.56 s\n",
      "Score on Test Data:  0.9736842105263158\n",
      "********** logistic_regression **********\n",
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 2.06 s\n",
      "Wall time: 2.14 s\n",
      "Score on Test Data:  0.9736842105263158\n",
      "********** random_forest **********\n",
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "CPU times: total: 6.64 s\n",
      "Wall time: 6.71 s\n",
      "Score on Test Data:  0.9736842105263158\n",
      "********** decision_tree **********\n",
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "CPU times: total: 297 ms\n",
      "Wall time: 300 ms\n",
      "Score on Test Data:  0.9736842105263158\n",
      "********** naive_bayes **********\n",
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
      "CPU times: total: 125 ms\n",
      "Wall time: 129 ms\n",
      "Score on Test Data:  1.0\n"
     ]
    }
   ],
   "source": [
    "pipelines = {\n",
    "    'knn' : Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', KNeighborsClassifier())\n",
    "    ]), \n",
    "    'svc' : Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', SVC())\n",
    "    ]),\n",
    "    'logistic_regression': Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', LogisticRegression())\n",
    "    ]),\n",
    "    'random_forest': Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', RandomForestClassifier())\n",
    "    ]),\n",
    "    'decision_tree': Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', DecisionTreeClassifier())\n",
    "    ]),\n",
    "    'naive_bayes': Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', GaussianNB())\n",
    "    ])\n",
    "}\n",
    "\n",
    "# Define parameter grid for each algorithm\n",
    "param_grids = {\n",
    "    'knn': [\n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__n_neighbors' : [i for i in range(3, 21, 2)], \n",
    "            'classifier__p' : [1, 2, 3]\n",
    "        }\n",
    "    ],\n",
    "    'svc': [\n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__kernel' : ['rbf'], \n",
    "            'classifier__C' : [0.1, 0.01, 1, 10, 100]\n",
    "        }, \n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__kernel' : ['poly'], \n",
    "            'classifier__degree' : [2, 3, 4, 5], \n",
    "            'classifier__C' : [0.1, 0.01, 1, 10, 100]\n",
    "        }, \n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__kernel' : ['linear'], \n",
    "            'classifier__C' : [0.1, 0.01, 1, 10, 100]\n",
    "        }\n",
    "    ],\n",
    "    'logistic_regression': [\n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__C': [0.1, 1, 10], \n",
    "            'classifier__penalty': ['l2']\n",
    "        }, \n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__C': [0.1, 1, 10], \n",
    "            'classifier__penalty': ['l1'], \n",
    "            'classifier__solver': ['liblinear']\n",
    "        }, \n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__C': [0.1, 1, 10], \n",
    "            'classifier__penalty': ['elasticnet'], \n",
    "            'classifier__l1_ratio': [0.4, 0.5, 0.6],\n",
    "            'classifier__solver': ['saga']\n",
    "        }\n",
    "    ],\n",
    "    'random_forest': [\n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__n_estimators': [50, 100, 200]\n",
    "        }\n",
    "    ],\n",
    "    'decision_tree': [\n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()],\n",
    "            'classifier__max_depth': [None, 5, 10]\n",
    "        }\n",
    "    ],\n",
    "    'naive_bayes': [\n",
    "        {\n",
    "            'scaler': [StandardScaler(), MinMaxScaler()]\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Perform GridSearchCV for each algorithm\n",
    "best_models = {}\n",
    "\n",
    "for algo in pipelines.keys():\n",
    "    print(\"*\"*10, algo, \"*\"*10)\n",
    "    grid_search = GridSearchCV(estimator=pipelines[algo], \n",
    "                               param_grid=param_grids[algo], \n",
    "                               cv=5, \n",
    "                               scoring='accuracy', \n",
    "                               return_train_score=True,\n",
    "                               verbose=1\n",
    "                              )\n",
    "    \n",
    "    %time grid_search.fit(X_train, y_train)\n",
    "    \n",
    "    best_models[algo] = grid_search.best_estimator_\n",
    "    \n",
    "    print('Score on Test Data: ', grid_search.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn\n",
      "Pipeline(steps=[('scaler', MinMaxScaler()),\n",
      "                ('classifier', KNeighborsClassifier(n_neighbors=3))])\n",
      "\n",
      "svc\n",
      "Pipeline(steps=[('scaler', MinMaxScaler()), ('classifier', SVC(C=1))])\n",
      "\n",
      "logistic_regression\n",
      "Pipeline(steps=[('scaler', StandardScaler()),\n",
      "                ('classifier',\n",
      "                 LogisticRegression(C=10, l1_ratio=0.4, penalty='elasticnet',\n",
      "                                    solver='saga'))])\n",
      "\n",
      "random_forest\n",
      "Pipeline(steps=[('scaler', StandardScaler()),\n",
      "                ('classifier', RandomForestClassifier(n_estimators=50))])\n",
      "\n",
      "decision_tree\n",
      "Pipeline(steps=[('scaler', MinMaxScaler()),\n",
      "                ('classifier', DecisionTreeClassifier())])\n",
      "\n",
      "naive_bayes\n",
      "Pipeline(steps=[('scaler', StandardScaler()), ('classifier', GaussianNB())])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, model in best_models.items():\n",
    "    print(f\"{name}\")\n",
    "    print(f\"{model}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** knn **********\n",
      "CPU times: total: 15.6 ms\n",
      "Wall time: 3.98 ms\n",
      "Accuracy Score 0.9736842105263158\n",
      "Model Size: 11965 Bytes\n",
      "********** svc **********\n",
      "CPU times: total: 0 ns\n",
      "Wall time: 1.99 ms\n",
      "Accuracy Score 0.9736842105263158\n",
      "Model Size: 5634 Bytes\n",
      "********** logistic_regression **********\n",
      "CPU times: total: 0 ns\n",
      "Wall time: 997 µs\n",
      "Accuracy Score 0.9736842105263158\n",
      "Model Size: 2142 Bytes\n",
      "********** random_forest **********\n",
      "CPU times: total: 0 ns\n",
      "Wall time: 3.99 ms\n",
      "Accuracy Score 0.9736842105263158\n",
      "Model Size: 83975 Bytes\n",
      "********** decision_tree **********\n",
      "CPU times: total: 0 ns\n",
      "Wall time: 1.99 ms\n",
      "Accuracy Score 0.9736842105263158\n",
      "Model Size: 3720 Bytes\n",
      "********** naive_bayes **********\n",
      "CPU times: total: 15.6 ms\n",
      "Wall time: 998 µs\n",
      "Accuracy Score 1.0\n",
      "Model Size: 2070 Bytes\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import os\n",
    "\n",
    "for name, model in best_models.items():\n",
    "    print(\"*\"*10, name, \"*\"*10)\n",
    "    \n",
    "    joblib.dump(model, f'best_models/{name}.pkl')\n",
    "    model = joblib.load(f'best_models/{name}.pkl')\n",
    "    \n",
    "    # new_data = np.array([[5.1, 3.0, 1.1, 0.1]])\n",
    "    %time y_test_pred = model.predict(X_test)\n",
    "    print(\"Accuracy Score\", metrics.accuracy_score(y_test, y_test_pred))\n",
    "    \n",
    "    print(\"Model Size:\", os.path.getsize(f'best_models/{name}.pkl'), \"Bytes\")"
   ]
  }
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